Activities per year
Abstract
The retina acts as the primary stage for the encoding of visual stimuli in the central nervous system. It is comprised of numerous functionally distinct cells tuned to particular types of visual stimuli. This work presents an analytical approach to identifying contrast-driven retinal cells. Machine learning approaches as well as traditional regression models are used to represent the input-output behaviour of retinal ganglion cells. The findings of this work demonstrate that it is possible to separate the cells based on how they respond to changes in mean contrast upon presentation of single images. The separation allows us to identify retinal ganglion cells that are likely to have good model performance in a computationally inexpensive way.
Original language | English |
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Title of host publication | Artificial Neural Networks and Machine Learning – ICANN 2021 - 30th International Conference on Artificial Neural Networks, Proceedings |
Editors | Igor Farkaš, Paolo Masulli, Sebastian Otte, Stefan Wermter |
Publisher | Springer Science and Business Media Deutschland GmbH |
Pages | 635-646 |
Number of pages | 12 |
ISBN (Print) | 9783030863647 |
DOIs | |
Publication status | Published - 13 Sept 2021 |
Event | 30th International Conference on Artificial Neural Networks, ICANN 2021 - Virtual, Online Duration: 14 Sept 2021 → 17 Sept 2021 |
Publication series
Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
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Volume | 12893 LNCS |
ISSN (Print) | 0302-9743 |
ISSN (Electronic) | 1611-3349 |
Conference
Conference | 30th International Conference on Artificial Neural Networks, ICANN 2021 |
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City | Virtual, Online |
Period | 14/09/2021 → 17/09/2021 |
Bibliographical note
Funding Information:The authors would like to thank Jian Liu, Tim Gollisch and the “Sensory Processing in the Retina” research group at the Department of Ophthalmology, University of Göttingen who supplied the experimental data as part of the “VISUALISE” project funded under the European Union Seventh Framework Programme (FP7-ICT-2011.9.11); grant number [600954] (“VISUALISE”).
Publisher Copyright:
© 2021, Springer Nature Switzerland AG.
Keywords
- Encoding natural images
- Identifying cell behaviour
- Retinal modelling
- Visual modelling
ASJC Scopus subject areas
- Theoretical Computer Science
- General Computer Science
Fingerprint
Dive into the research topics of 'Computational Approach to Identifying Contrast-Driven Retinal Ganglion Cells'. Together they form a unique fingerprint.Activities
- 1 Participation in conference
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24th Irish Machine Vision and Image Processing Conference 2022
Gault, R. (Conference committee chair), Porter, V. (Member of the organising committee), Lydon, D. (Member of the organising committee), McCombe, K. (Member of the organising committee), McWhirter, R. (Member of the organising committee) & Fahim, M. (Member of the organising committee)
31 Aug 2022 → 02 Sept 2022Activity: Participating in or organising an event types › Participation in conference
Research output
- 1 Article
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Multi-channel auto-encoders for learning domain invariant representations enabling superior classification of histopathology images
Moyes, A., Gault, R., Zhang, K., Ming, J., Crookes, D. & Wang, J., Jan 2023, In: Medical Image Analysis. 83, 10 p., 102640.Research output: Contribution to journal › Article › peer-review
Open AccessFile13 Citations (Scopus)129 Downloads (Pure)